docs: def batch_scan_layers(self, cells: np.ndarray = None, genes: np.ndarray = None, axis: int = 0, batch_size: int = 1000, layers: Iterable = None) -> Iterable[Tuple[int, np.ndarray, Dict]]:
  		"""
  		**DEPRECATED** - Use `scan` instead
  		"""
  		deprecated("'batch_scan_layers' is deprecated. Use 'scan' instead")
  		if cells is None:
  			cells = np.fromiter(range(self.shape[1]), dtype='int')
  		if genes is None:
  			genes = np.fromiter(range(self.shape[0]), dtype='int')
  		if layers is None:
  			layers = self.layers.keys()
  		if axis == 1:
  			cols_per_chunk = batch_size
  			ix = 0
  			while ix < self.shape[1]:
  				cols_per_chunk = min(self.shape[1] - ix, cols_per_chunk)
  
  				selection = cells - ix
  				# Pick out the cells that are in this batch
  				selection = selection[np.where(np.logical_and(selection >= 0, selection < cols_per_chunk))[0]]
  				if selection.shape[0] == 0:
  					ix += cols_per_chunk
  					continue
  
  				# Load the whole chunk from the file, then extract genes and cells using fancy indexing
  				vals = dict()
  				for key in layers:
  					vals[key] = self.layers[key][:, ix:ix + cols_per_chunk]
  					vals[key] = vals[key][genes, :]
  					vals[key] = vals[key][:, selection]
  
  				yield (ix, ix + selection, vals)
  				ix += cols_per_chunk
  
  		if axis == 0:
  			rows_per_chunk = batch_size
  			ix = 0
  			while ix < self.shape[0]:
  				rows_per_chunk = min(self.shape[0] - ix, rows_per_chunk)
  
  				selection = genes - ix
  				# Pick out the genes that are in this batch
  				selection = selection[np.where(np.logical_and(selection >= 0, selection < rows_per_chunk))[0]]
  				if selection.shape[0] == 0:
  					ix += rows_per_chunk
  					continue
  
  				# Load the whole chunk from the file, then extract genes and cells using fancy indexing
  				vals = dict()
  				for key in layers:
  					vals[key] = self.layers[key][ix:ix + rows_per_chunk, :]
  					vals[key] = vals[key][selection, :]
  					vals[key] = vals[key][:, cells]
  				yield (ix, ix + selection, vals)
  				ix += rows_per_chunk